Effect of Improved Management Practices on Soil Organic Carbon Sequestration in Wheat-maize Double Cropping System in North China
Bibliographic record
Abstract
Carbon sequestration in cropland soils which could be achieved through improved management practices (IPMs) represents an important opportunity to offset a portion of greenhouse gas emissions. North China is the main wheat and maize production region where many IMPs have been widely used during the last several decades, but the effect size and duration of IMPs on soil organic carbon (SOC) sequestration in wheat-maize double cropping system in this region is scarcely studied. In this study, a meta-analysis was conducted to compare the effect size and duration of four IMPs on SOC sequestration in wheat-maize double cropping system in north China. A total of 29 long-term experiments, consisting of 119 paired treatments were compiled in this analysis. The results indicated that the four IMPs of organic manure application (OM), organic manure combined with chemical fertilizer application (MF), straw return (SR) and reduced or no tillage (RNT) all had significant effects on SOC sequestration in the study area. On average, the IMPs of OM, MF, SR and RNT enhanced SOC density by 260, 328, 278 and 134 kg ha-1 yr-1, respectively. The effect duration of OM, MF, SR and RNT on SOC sequestration were about 48, 26, 22 and 18 years, respectively. Accumulation enhancements of SOC for OM, MF, SR and RNT over SOC sequestration period were about 34.7%, 36.1%, 22.0% and 12.7%, respectively. OM and MF could be the appropriate practices on SOC sequestration in wheat-maize double cropping system in the research area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".